Dangerous Speech and Dangerous Ideology: An Integrated Model for Monitoring and Prevention
Bibliographic record
Abstract
There is considerable agreement amongst scholars and international actors that ideologies and speech play a critical role in the path of escalation towards mass atrocity crimes. Speech features prominently in the jurisprudence of the U.N. war crimes tribunal for Rwanda, for example, and in historical accounts of the months and years preceding many other genocides. Nonetheless, this is one of the most underdeveloped components of genocide and atrocity prevention, in both theory and practice. This paper draws together the authors’ independent past work on dangerous speech and the ideological dynamics of mass atrocities by offering a new integrated model to help identify the sorts of speech and ideology that raise the risk of atrocities and genocides. We suggest that this model should inform monitoring activities concerned with the risk of genocides and mass atrocities, and prevention efforts at the strategic and targeted levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".